179 results about "Sensor model" patented technology

The invention discloses a pipe network operation and maintenance management system based on data analysis, which relates to the technical field of municipal infrastructure operation and maintenance management and comprises an equipment feature modeling module, a group anomaly identification module, a common-mode risk tracing module, a dynamic risk modeling module, a regulation and control strategy optimization module and a closed-loop self-adaptive updating module. Based on historical operation data and real-time acquisition parameters, feature coding is performed on the model, batch, position and time sequence of the sensor, and an equipment attribute mapping matrix is constructed. According to the method, the equipment attribute mapping matrix and the group anomaly identification mechanism are constructed, and external induction factor positioning and the dynamic risk weight map are combined, so that intelligent adjustment of regulation and control parameters and anomaly interference avoidance are realized, a closed-loop optimization process is constructed, and the identification accuracy, the control safety and the operation toughness of a pipe network system in a multi-disturbance scene are improved.

The invention provides a map construction method and system based on laser vision dynamic weighted fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining an RGB image and a depth image through a vision sensor, and obtaining point cloud data through a laser radar; aligning the depth image with the RGB image, and removing invalid depth pixels; based on an ORB-SLAM2 algorithm, performing visual SLAM, extracting features of the depth image and the RGB image, and outputting sparse visual point cloud data; based on a Gmapping algorithm, performing laser radar SLAM, and outputting a 2D occupied grid map; projecting the sparse visual point cloud data into a 2D occupation grid map, and calculating the semantic occupation probability of each grid; calculating the geometric occupancy probability of each grid through an anti-sensor model; performing weighted fusion on the semantic occupancy probability and the geometric occupancy probability, and calculating a fusion occupancy probability; and generating a fusion map according to the fusion occupation probability.

The invention provides a dynamic fault-tolerant control method based on an expected function safety mechanism, and the method comprises the steps: 1, selecting the model of a sensor, and enabling the sensor to be distributed at the periphery of a vehicle body; 2, building a decision algorithm model, carrying out environment scene recognition, planning a driving path and speed according to data obtained by a sensing layer, evaluating safety risks of different decisions, and carrying out judgment and decision making on various conditions; 3, control interfaces of a vehicle power system, a steering system and a braking system are established, and the electronic control unit converts a decision control instruction output by the decision algorithm model into actual vehicle operation through the control interfaces; 4, performing an expected function safety mechanism, performing real-time monitoring on state parameters of the vehicle, and analyzing detection data through a fault detection algorithm; 5, implementing a fault tolerance strategy; and 6, continuously monitoring the actual driving state and the system response condition of the vehicle, and adjusting the fault tolerance strategy.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

The invention discloses a satellite ground staring attitude control multi-software joint simulation system and a construction method thereof, and belongs to the technical field of computer simulation, and the construction method comprises the steps: 1, constructing an ADAMS-based microsatellite ontology model; 2, constructing a satellite orbit model and an attitude sensor model based on STK; step 3, constructing a torque execution mechanism model based on MATLAB (matrix laboratory) and Simulink; 4, designing an attitude determination algorithm and a control algorithm of a ground staring task simulation model capable of combining a microsatellite body model, a satellite orbit model, an attitude sensor model and a torque execution mechanism model; according to the method, ADAMS, STK and MATLAB are combined to simulate a satellite body, an attitude sensor, a torque execution mechanism and a running orbit, the attitude control task of the satellite is simulated, and the method has the advantages of being good in simulation effect and visual and intuitive in simulation process.

The invention relates to a nuclear power plant solenoid valve life prediction method and terminal equipment. The method comprises the following steps: based on a set sequence model and an attention mechanism, establishing a multi-layer perceptron model which can perform sequence data processing and perform training by pertinently utilizing set feature data; obtaining a training set and a verification set, training the multi-layer perceptron model by using the training set, determining hyper-parameters of the multi-layer perceptron model by using the verification set, and obtaining the trained multi-layer perceptron model; performing model distillation processing on the trained multi-layer sensor model to obtain a prediction model capable of predicting the service life of the electromagnetic valve; acquiring characteristic data of the tested electromagnetic valve; and inputting the feature data into the prediction model to obtain a prediction result output by the prediction model. The service life of the electromagnetic valve is predicted, and the prediction model has the advantages of being simple in structure and high in prediction accuracy.

The invention discloses a bridge maintenance operation supervision method, and belongs to construction site management, and the method comprises the steps: building a sensor model, a personnel model and a bridge BIM model; registering the sensor model and the bridge BIM model by using a geographic coordinate system; integrating a personnel model, the registered sensor model and the bridge BIM model through a GIS platform to obtain a digital twin scene based on BIM + GIS; the method comprises the following steps: acquiring GPS coordinate data of an operator through an intelligent safety helmet, and converting the GPS coordinate data into coordinate data under a Cartesian coordinate system; establishing a multi-source data correction mechanism, and correcting the coordinate data under the Cartesian coordinate system; and according to the corrected coordinate data, the position of the operator is displayed in real time in the digital twinborn scene through the operator model. In order to solve the problem that the positioning precision of operators is low due to GPS signal drifting in bridge maintenance, the monitoring and positioning precision is improved.

Satellite images have inherent geo-positional errors of orders a few meters. Corrections are achieved by adjusting a sensor model which maps ground coordinates of control features into image coordinates and establishing a correspondence between the ground and image features, in this case a road network. The ground coordinates are obtained from mobile pose points. To adjust the sensor model we rely on the fact that the roads are typically much more uniform than surrounding features, and therefore have smaller entropy. The sensor model is adjusted so that the image pixels, obtained from projecting ground coordinates of the mobile pose points onto the image, minimize the entropy of the pixels that represent the road network.

The invention discloses an IEC61850 configuration import and Internet of Things uplink configuration method and a gateway machine, and when 61850 communication protocol data of a certain manufacturer needs to be accessed to the gateway machine, firstly, a CID file is loaded, and the CID file is loaded into the gateway machine; reading a server-side IP address, a logic node type, a data object type, an instantiated logic node device, an instantiated data object list and an instantiated data object path; then, communication node configuration between the gateway machine and the 61850 server side of the manufacturer is selected through the IP address of the server side; then, respectively establishing a mapping relationship between the logic node type, the data object type and a preset data model of the gateway machine and a mapping relationship between data points of the data model; then, generating a sensor model corresponding to the instantiated sensor; finally, an instantiated sensor data point uplink data transmission configuration is generated based on the sensor data point uplink data transmission configuration in the sensor data point uplink configuration module. According to the method, the import configuration problem of 61850 models of different manufacturers is solved, and the problem that protocol data points need to be repeatedly configured during import of different manufacturers is also solved.

A system and method for placing and configuring physical security sensors & barriers, are described. The system generates and analyzes a three-dimensional model of a customer's premises using non-imaging sensors to determine optimal sensor placement. Based on customer requirements, the system distributes sensor models within the premises while accounting for environmental obstructions, coverage gaps, and redundancy minimization. An extended reality (XR) interface allows users to visualize and refine sensor configurations before deployment. The system integrates AI-driven analytics to optimize surveillance coverage, adjust sensor orientations dynamically, and generate detailed reports outlining sensor locations and configurations. The sensor stand transmits real-time data to the system for continuous monitoring, predictive maintenance, and security threat analysis. The system enhances security planning by reducing design time, installation costs, preventing post-deployment modifications, and ensuring comprehensive monitoring coverage across diverse environments, including commercial, industrial, and critical infrastructure sites.

The invention discloses a multi-robot cooperative positioning and elastic formation control method based on a distributed Kalman consistency filtering (DKCF) algorithm. Aiming at the problems of insufficient sensor noise suppression, poor robustness under network attack and the like in the prior art, a dynamic model containing process noise and a sensor model of measurement noise are constructed, and a consistency state estimation method of weighted fusion observation data is provided. According to the method, multi-modal redundant sensor information is fused, and an improved DKCF is combined, so that cooperative state estimation between robots is realized, and the positioning precision in a noise environment is effectively improved; an elastic positioning framework under denial of service attack is established, and the robustness to network attack is enhanced while the formation precision is ensured through the joint design of distributed cooperative position estimation and a formation controller. According to the method, high positioning precision and a stable formation form can still be kept in the face of network attacks of denial of service, and the safety cooperation performance of a multi-robot system in a complex scene is remarkably improved.

The invention relates to a real-time azimuth tracking model-based algorithm based on a horizontal circular array. The real-time azimuth tracking model-based algorithm comprises the following steps: S1, determining equipment parameters and water area environment parameters for measuring a moving target; s2, constructing an azimuth angle state space equation and a calculation formula of a sensor model; s3, measuring the background noise level of the water area, and giving a noise coefficient of a measurement equation; s4, setting an azimuth angle initial value by observing the approximate direction of the moving target by a measurer, or estimating the azimuth angle initial value through a conventional beam forming method; and S5, performing calculation through an unscented Kalman filter azimuth tracking algorithm framework, and solving the azimuth angle of the moving target in real time. According to the unscented Kalman filtering framework, the input is sound pressure real-time data, the output is a moving target azimuth angle, the online real-time performance of operation is ensured, meanwhile, a measurement equation based on a plane wave propagation model is constructed according to the spatial position of a horizontal circular array, and by means of the isotropic property of the circular array, the real-time performance of the horizontal circular array is improved. And the reliability of estimation results of the algorithm in all directions is ensured.

This invention provides a method for modeling the starting performance of a marine gas turbine. First, a gas turbine aero-thermodynamic performance calculation model is established using C++, and a heat transfer model, temperature sensor model, and control law model for the gas turbine's high-temperature components are established using the M language. Then, based on test data from the gas turbine startup process, the method analyzes how the pressure ratio and efficiency of typical gas turbine components change with the physical speed of the gas generator during startup. The corresponding low-speed characteristics of the gas turbine's rotating components are generated and corrected, enabling high-precision, real-time simulation of the gas turbine's starting performance. This method considers the heat exchange between the high-temperature fuel gas and the ambient atmosphere and the gas turbine itself, improving the accuracy of the gas turbine startup performance simulation. The method can provide a simulation testing tool for the design and optimization of gas turbine startup control laws.

A sensor for detecting hazards is described that includes a memory and a processor. The processor may be configured to generate sensor profile data associated with a location and apply the sensor profile data to a sensor model profile associated with the location wherein the sensor model profile includes a plurality of parameter levels for the location generated by a machine learning model. The processor may also be configured to identify a discrepancy between the sensor profile data and the sensor model profile and determine a potential hazard at the location based upon the discrepancy between the sensor profile data and the sensor model profile. The processor may further be configured to generate an alert based upon the potential hazard at the location.

The invention discloses a device and a method for measuring dynamic response characteristic parameters of a temperature sensor, and belongs to the technical field of temperature measurement. The measuring device comprises a convection environment, a control unit, and a displacement sensor and an electromagnetic ejection device which are connected with the control unit; the electromagnetic ejection device comprises a guide rail and a conductive sliding block, the conductive sliding block is in sliding connection with the guide rail, the conductive sliding block is used for being connected with a to-be-tested temperature sensor, the to-be-tested temperature sensor directly faces the measuring hole of the convection environment, and the conductive sliding block can move towards the measuring hole of the convection environment under the electromagnetic action; the displacement sensor is arranged at the edge of a measuring hole of the convective environment and used for acquiring the moment when the temperature measuring end of the temperature sensor to be measured enters the convective environment, and the temperature sensor to be measured is connected with the control unit; an environment control device is arranged in the convection environment and is used for regulating and controlling environment parameters of the convection environment; the measuring device can accurately determine the dynamic response characteristic parameters of the temperature sensor, and facilitates sensor model selection in practical application.

The invention relates to a method, in particular a computer-implemented method, for providing a data-based sensor model for use as a virtual sensor in a technical system, having the following steps: - providing training data sets that result from a test bench measurement, wherein each training data set assigns one or more state variables, in particular state variables recorded using sensors, and / or one or more predefined operating variables to a plurality of labels at a particular time, wherein the labels comprise one or more measured sensor variables to be modelled and one or more additional variables; - training the data-based sensor model with the aid of the training data sets; - implementing the sensor model in a control unit of the technical system, such that only the one or more sensor variables to be modelled are used as virtual sensors during model evaluation.

The invention belongs to the technical field of multi-unmanned aerial vehicle collaborative planning, and particularly relates to a multi-unmanned aerial vehicle intelligent avoidance target searching method based on prior probability information guidance, which comprises the following steps: step 1, establishing an unmanned aerial vehicle platform model, a sensor model and a target motion model; 2, an environment information graph model is established, wherein the environment information graph model comprises a target search probability graph, an environment uncertain information graph and a digital pheromone graph; and step 3, performing path planning by combining particle swarm optimization (PSO) and a Dubins curve under a distributed model predictive control (DMPC) framework, tracking the target by the unmanned aerial vehicle according to avoidance dynamics of the target, and stopping moving once the target is captured by the unmanned aerial vehicle until all the targets are captured. According to the method, the motion prediction model is established, the probability map is updated in real time, efficient search of multiple targets is achieved under the condition that the number of the targets, the initial positions and the motion states are known, and the problems that a collaborative search algorithm is low in efficiency and resources of multiple unmanned aerial vehicles are wasted are solved.

The invention discloses a simulation debugging method and device for a wireless Lora sensor in a power transformation and distribution station room, and the method comprises the steps: creating a sensor equipment model for the wireless Lora sensor of each sensor type in a gateway machine, and defining the parameter information of the sensor model and the parameter information of a model data point; the parameter information of the sensor model comprises a unique code of the model; configuring sensor data points in a configuration file of the wireless Lora message simulator, constructing a mapping relation between a sensor type serial number and a model unique code, and further establishing a one-to-one correspondence relation between model data points and the sensor data points; in a wireless Lora message simulator, firstly, sensor equipment and sensor data points are added in a mode of loading a sensor equipment model, then, data point values meeting parameter information requirements of the sensor data points are generated through simulation, and finally, the data point values generated through simulation are sent according to a set message sending mode. According to the method, the wireless LoRa sensor can be simulated to generate the data point value of the sensor, and the problem of low data message development and test efficiency caused by dormancy of the wireless LoRa sensor or incomplete type of the wireless LoRa sensor is solved.

The invention discloses an LKA function simulation test method and system considering man-machine interaction, and relates to the technical field of automobile intelligent driving testing, and the method comprises the steps: building a simulation model based on an upper computer system, and operating the simulation model through a lower computer system; scene simulation and configuration of a sensor model are carried out through a graphic workstation, and scene image data output by the graphic workstation are received in a video injection system and sent to a controller to be tested; a real EPS rack is used for executing transverse control related instructions, hand force of a steering wheel is fed back to a servo control system in a driving simulation cabin, and reproduced hand torque is transmitted to a real driver through a mechanical steering system. According to the method, the problem of insufficient environmental perception precision in a traditional simulation test is solved, the real-time performance of image data is guaranteed, the requirement of the to-be-tested controller for high timeliness of sensor data is met, the authenticity and reliability of an LKA function test are improved, and data support is provided for human-computer interaction optimization.

The invention discloses a method for predicting the survival rate of a patient with heart implantation equipment, which is used for predicting the survival rate of the patient with the heart implantation equipment through a trained heart implantation prognosis model. The cardiac implantation prognosis model construction method comprises the following steps: obtaining clinical data of a patient with cardiac implantation equipment, and preprocessing the clinical equipment; selecting prediction features for predicting the survival rate of the patient with the heart implantation equipment from the existing clinical features; taking the patient data with the prediction features as a training sample set to train a prognosis model based on a multi-layer perceptron model so as to obtain the prognosis model; the prognosis model is optimized by adopting 5-fold cross validation.

Model selection is disclosed. Features used as inputs to models are scored in terms of importance and health. The importance and health scores are combined in order to generate a model score for each model. The model with a score above a threshold score is selected and deployed.

The invention provides an automatic driving live-action simulator based on three-dimensional Gaussian sputtering, and the simulator comprises a general model interface module which achieves the integration of different Gaussian models, sensor models and vehicle kinematics models through providing a standardized interface, and obtains basic elements and self-vehicle elements in a simulation scene; the internal scene representation module is used for defining and organizing the basic elements in a structured mode to obtain environment elements; the collision detection module is used for distinguishing foreground objects and backgrounds of the environment elements, respectively carrying out foreground collision detection and background collision detection, and carrying out collision judgment on the self-vehicle elements and the environment elements in the simulation scene; and the simulation control module is used for performing dynamic response of the simulator to the external control signal based on the internal scene representation module and the collision detection module. The method can meet the requirements of the automatic driving algorithm for massive high-quality data, and is suitable for training and testing of the automatic driving algorithm.

The present application discloses a method for intelligent escort of a high-value spacecraft cluster, belonging to the technical field of spacecraft, including: obtaining the initial data of the spacecraft cluster, where the spacecraft cluster includes multiple spacecraft with autonomous decision-making; according to the initial data, combining the spacecraft orbit model, satellite on-board sensor model, and illumination model to conduct environmental deduction on the spacecraft cluster and obtain the current simulated environment; constructing a behavior rule library and a capability rule library for the spacecraft cluster; setting a reward function, establishing a reinforcement learning model according to the multi-agent reinforcement learning algorithm and the reward function, and iteratively training the reinforcement learning model according to the current simulated environment, behavior rule library, and capability rule library until the reinforcement learning model converges; simulating the tasks in the real scenario according to the converged reinforcement learning model to obtain the final multi-agent decision result and performing visualization processing. Making decisions for the problem of spacecraft cluster approaching can avoid the radiation or interference of other spacecraft.

The invention discloses a special simulation test method for an automatic driving perception algorithm, and the method comprises the steps: building a simulation vehicle sensor model and a simulation scene, and operating a simulator and outputting a simulation sensor signal in combination with the sensor model and the simulation scene; performing off-line training and on-line testing by using the simulation sensor signal, and respectively obtaining a model parameter weight for updating the automatic driving perception algorithm and an effect evaluation result of the simulation testing; optimizing a sensing algorithm by using the weight information, and adjusting the configuration and installation position of each sensor on the real vehicle based on an effect evaluation result obtained by the output of the optimized sensing algorithm; and based on the adjusted real vehicle sensor deployment, reconstructing a sensor model and carrying out simulation test again. According to the invention, a large amount of test data can be obtained at low cost, various uncontrollable real scenes can be reproduced, the configuration and the installation position of the sensor can be changed according to the targeted requirements of a sensing algorithm, and the refitting cost and the time cost of a test vehicle are greatly saved.

A system and method for placing and configuring physical security sensors & barriers, are described. The system generates and analyzes a three-dimensional model of a customer's premises using non-imaging sensors to determine optimal sensor placement. Based on customer requirements, the system distributes sensor models within the premises while accounting for environmental obstructions, coverage gaps, and redundancy minimization. An extended reality (XR) interface allows users to visualize and refine sensor configurations before deployment. The system integrates AI-driven analytics to optimize surveillance coverage, adjust sensor orientations dynamically, and generate detailed reports outlining sensor locations and configurations. The sensor stand transmits real-time data to the system for continuous monitoring, predictive maintenance, and security threat analysis. The system enhances security planning by reducing design time, installation costs, preventing post-deployment modifications, and ensuring comprehensive monitoring coverage across diverse environments, including commercial, industrial, and critical infrastructure sites.

The application discloses a kind of HIL simulation methods of underground mining whole process, it is related to the field of autonomous driving simulation test, including: the point cloud data of underground scene is collected, and CARLA format simulation scene is generated;The generated CARLA format simulation scene is compiled and imported by Unreal engine, vehicle model and laser radar sensor model are built in Carla simulator, and the point cloud data packet of simulation scene is collected;According to the point cloud data packet, mapping is carried out by SLAM algorithm, and the point cloud map of PCD format is generated;According to the PCD point cloud map, the map of simulation scene is constructed;Hardware-in-the-loop HIL simulation platform including host computer, lower computer and actuating mechanism is built;The map of simulation scene constructed is loaded to the hardware-in-the-loop HIL simulation platform constructed, and the real mine working condition scene is simulated through simulation platform, and the mine working condition unmanned driving algorithm is verified.The present application improves the coverage of test by whole process closed loop etc., for the difficulty of comprehensive evaluation unmanned driving test in prior art.

The invention discloses a low-illumination image reconstruction method and system based on deep learning, and the method comprises the following steps: S1, constructing a bidirectional image conversion and processing flow reconstruction network and an adaptive exposure synthesis network, and collecting image samples to train the two networks; and S2, connecting the bidirectional image conversion and processing flow reconstruction network and the adaptive exposure synthesis network, and modeling the noise by using a low-illumination sensor model to obtain a synthesized low-illumination image. And S3, training a preset U-net network by using the synthesized low-illumination image. And S4, inputting the real low-illumination image into the trained U-net network for reconstruction to obtain a low-illumination reconstructed image. Compared with the prior art, the problems that the image is difficult to mutually convert from the RGB domain to the RAW domain and the exposure level is adaptively adjusted when the low-illumination image is synthesized are solved.

The invention provides a fluid control device, a control unit for the fluid control device, a fluid control method, and a storage medium, which can realize high-speed response and high-precision flow rate control, and an observer determines the flow rate of the fluid based on the deviation between a flow rate measurement value output by a flow rate sensor and a flow rate estimation value output by a flow rate sensor model. A model error estimation value that estimates a model error of the flow sensor model with respect to the flow sensor and a flow error estimation value that estimates a flow error due to a change in the fluid control valve are obtained, and the model error estimation value is input into the flow sensor model. And outputs an estimated value to a feedback controller on the basis of the flow rate error estimated value and a valve flow rate estimated value output by the valve model.

The present invention provides a motor controller and its motor temperature sampling circuit. The motor temperature sampling circuit includes a main sampling branch and an auxiliary sampling branch. The input ends of both are connected to the output end of the motor temperature sensor, and the output ends of both are respectively connected to two input ends of a processor in the motor controller. Moreover, the main sampling branch includes a differential amplification link that the auxiliary sampling branch does not have. Therefore, the auxiliary sampling branch can respond to the change of the output voltage of the temperature sensor faster than the main sampling branch. The two output signals are compared in real time in the processor. When the motor temperature sensor has a disconnection, the difference between the two output signals is greater than a preset threshold, thereby realizing the rapid detection of the disconnection of the temperature sensor line. Moreover, the motor temperature sampling circuit can also automatically identify the sensor model by adjusting the voltage division parameters and the gain coefficient of the differential amplification link.

A simulation device includes: a model storage unit that stores a physical model of a physical system configured to generate a physical signal waveform, a sensor model, a machine learning model, and an abnormality determination model; a model calculation unit configured to perform calculation processing using the physical model, the sensor model, the machine learning model, and the abnormality determination model; and a model setting unit configured to perform settings relating to each of the physical model, the sensor model, the machine learning model, and the abnormality determination model on the basis of an input from an operation input unit.